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A high‐resolution wave model is crucial for accurate modeling of sediment and organic material transports, but its computational costs hinder direct coupling to an ecosystem model. We developed a machine learning model using long short‐term memory to simulate large‐scale, high‐resolution waves. Trained with numerical wave model (NWM) outputs and wind data from nine locations, our model successfully replicates NWM results for daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution. Compared to the NWM, the data‐driven model has root‐mean‐square errors below 6 cm for daily mean significant wave height and 1 s for the wave period in the bay. It demonstrates excellent model skills and can accurately forecast daily mean significant wave height and period at NOAA wave stations comparable to NWMs. Using minimal wind data and having a short runtime, our data‐driven model shows promise as an alternative for wave forecasting and coupling with sediment and ecological models. Plain Language Summary: Accurately modeling sediment and organic transport necessitates the use of a high‐resolution numerical wave model (NWM). However, the computational expenses of running an NWM within an ecosystem model pose significant challenges. We conducted a study using a machine learning approach based on long short‐term memory to replicate the results of an NWM. The data‐driven wave model (DWM) was trained with the NWM outputs and wind data from nine locations. The trained DWM can replicate the NWM results of daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution and the same predictive skill with a root‐mean‐square error of less than 6 cm and 1 s, respectively for the model domain. The model successfully forecasted daily mean significant wave height and period at NOAA wave stations, showing great forward prediction capability. Notably, the DWM only relied on a minimal amount of wind data from selected locations and had a short runtime. These findings indicate that machine learning is promising as an alternative approach to NWM for wave forecasting. Moreover, it can serve as an alternative wave model and be coupled to ecological models for simulating sediment transport, light attenuation, and resuspension of organic materials. Key Points: A machine learning‐based wave model reproduces the numerical wave model (NWM) results at the same spatial resolution with good accuracyThe data‐driven model, trained on daily significant wave height and period using NWM outputs, has comparable wave prediction skillThe fast‐running data wave model, driven by wind data from nine locations, can forecast waves and couple with sediment and ecological models [ABSTRACT FROM AUTHOR]

Feedbacks between sediment and plants in the submersed aquatic vegetation (SAV) beds of the Susquehanna Flats help modulate sediment delivery into the upper Chesapeake Bay from its major tributary (Susquehanna River). Recent modeling work has shown that SAV can steer the river plume, directly controlling sediment transport and fate. However, transport mechanisms likely differ between flood and non‐flood conditions and depend on event timing. An opportunity to evaluate these insights in the field occurred with several flood events in summer 2018. Sediment and biomass samples were collected after the flood in the large, continuous main SAV bed of the Flats and in smaller patches to the west of the main bed to evaluate the effect of SAV bed size (width). Sediment characteristics (grain size, organic content) and deposition rates were compared to previous observations during non‐flood conditions in the main bed in 2014–2015 and in the smaller patches in 2019. Overall, sediment deposition rates could be predicted by an empirical model including sediment load, vegetation biomass, and SAV bed width, reflecting the role of vegetation in steering the Susquehanna River plume and modulating sediment input to the upper Chesapeake Bay. Understanding spatiotemporal patterns of sedimentation is essential for realistic estimates of sediment and nutrient (especially nitrogen and carbon) storage in freshwater SAV systems, which have not received as much attention as their saltwater counterparts. [ABSTRACT FROM AUTHOR]

Community-level responses of soft sediment macrobenthos to two relatively large-scale disturbance events associated with dredged material (DM) disposal are examined for subtidal (>10 m) lower Chesapeake Bay. Disturbance severity (DM thickness on initial sampling date following disposal) and date of sampling were important factors explaining the patterns and rates of recovery for species richness, abundance, biomass, and community composition, but sediment disposal had minimal effects when DM thickness was ≤15 cm. It took 1.5 years or less following the cessation of disposal activities for richness, abundance, biomass and community composition at high disposal severity (DM > 15 cm) to attain levels measured at reference stations representing the ambient community of the region. Positive correlations of community structure metrics between the disposal area and reference stations provide evidence that non-local processes influenced patterns of recovery in this estuarine setting. Species interactions and food limitation may also have been important at local scales. [ABSTRACT FROM AUTHOR]

Salt marsh evolution is strongly affected by tidal processes and ecology, which regulate sediment accretion and erosional rates. A balance between marsh erosion and deposition in a restored tidal wetland is crucial for analyzing restoration strategies to adopt in a natural context. Here, we present an integrated approach monitoring salt marsh seasonal changes over several months in a microtidal restored salt marsh of the Paul S. Sarbanes Ecosystem Restoration Project at Poplar Island (MD, USA). The project is undertaken at a restoration site where sediment dredged from the shipping channels in the upper Chesapeake Bay is being used to restore a tidal marsh habitat in mid-Chesapeake Bay. We flew an Unmanned Aerial Vehicle (UAV) with an RGB and a multispectral camera to obtain a high-resolution map of the planimetric position of vegetation and to monitor the health of the marsh vegetation in diverse seasons. Due to its extension of 400 m by 400 m, a total of four flight plans were necessary to cover the entire marsh flying at a 40 m altitude obtaining a 2 cm Ground Sample Distance (GSD). This technique provides reliable results at a very low cost, enabling an accurate assessment of the marsh platforms to be conducted over time, due to both the very high spatial resolution and the precise georeferencing of the images for the comparisons. Our results show seasonal variability in the two dominant species colonizing the low marsh, Spartina alterniflora, and high marsh, Sporobolus pumilus. While the lower marshes showed a higher variability along seasons, the up-land vegetation showed persistent green foliage during cold seasons. Detecting salt marsh evolution and seasonality coupled with field measurements can help to improve the accuracy of hydrodynamic and sediment transport models. Understanding the drivers of salt marsh evolution is vital for informing restoration practices and designs, in order to improve coastal resilience, and develop and coastal management strategies. [ABSTRACT FROM AUTHOR]